Multi-frame super resolution reconstruction technology is used to fuse a set of observed low resolution images into one high resolution image. Due to sub-pixel shifts, each observed low resolution image contains complementary information. With knowledge of the shifts, these low resolution images can be combined to remove the aliasing and generate a higher resolution image.
In conventional multi-frame algorithms, it is essential to know the shift of the sub-pixels between low resolution images. Thus, accurate motion estimation plays a critical role in conventional multi-frame fusion. But unavoidable motion estimation errors lead to disturbing artifacts. To avoid motion estimation, a super resolution algorithm free of the motion estimation is proposed, which averages neighbors by measuring similarity to reconstruct the center pixel. But the algorithm only takes translation into account. Since complex motions are usually contained in video, and even rotation exists in textures in one frame, the number of potential similar blocks that could be found by the algorithm will be reduced. Based on the non-local super resolution reconstruction or non-local denoising algorithm, two improvements are proposed: adaptive parameters selection and invariance-based similarity measure.
Adaptive parameters selection is to discuss the relationship among block sizes, search window sizes and performances of non-local means, and then to adaptively select these parameters. However, the mobile window search strategy in this algorithm is pixel-wise and is likely to be trapped into local minimum, thereby reducing the resolution of reconstructed image.
In summary, the resolution of an image after non-locality-based super resolution reconstruction is low in the state-of-art.